Discovering knowledge from large databases using prestored information

Information Systems

Volumn 26, Issue 1, 2001, Pages 1-14

  Tsai, Pauray S M ^a   Chen, Chien Ming ^b  

^a MINGHSIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b Silicon Integrated Systems group   (Taiwan)

Author keywords

Association Rules; Data Mining; Knowledge Discovery; Sequential Patterns

Indexed keywords

ALGORITHMS; DATA MINING; DATABASE SYSTEMS; INFORMATION RETRIEVAL;

PRESTORED INFORMATION; SEQUENTIAL PATTERNS;

KNOWLEDGE ACQUISITION;

EID: 0035278753     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0306-4379(01)00006-0     Document Type: Article

References (15)

1. Online generation of association rules
2. An interval classifier for database mining applications
3. Database mining: A performance perspective
4. Mining association rules between sets of items in large databases
5. Fast algorithms for mining association rules
6. Mining sequential patterns
7. Data mining: An overview from a database perspective
8. Data-driven discovery of quantitative rules in relational databases
9. Discovery of multiple-level association rules from large databases
10. Using a Hash-based method with transaction trimming for mining association rules
11. An efficient algorithm for mining association rules in large databases
12. Mining generalized association rules
13. Mining quantitative association rules in large relational tables
14. Mining sequential patterns: Generalizations and performance improvements